4 ms·
I agree that 2500x48hrs is probably a reasonably cost to pay for these kind of sweet results. But it is a bit prohibitively expensive for an ML hobbyist to try
by eachro 8y ago
I agree that 2500x48hrs is probably a reasonably cost to pay for these kind of sweet results. But it is a bit prohibitively expensive for an ML hobbyist to try to replicate in their own free time. I wonder if there is some way to do this w/o all the expensive compute. Pre-trained models is one step towards this, but so much of the learning(for the hobbyist) comes from struggling to get your RL model off the ground in the first place.
- boulos 8y agoIt'd be interesting to see in the graphs (when the OpenAI team gets to them) how good you get at X hours in. Because if you're pretty good at X=4, that's still amazing. Edit: I guess https://blog.openai.com/content/images/2018/06/bug-comparison-small@2x.png https://blog.openai.com/content/images/2018/06/bug-compariso... is approximately indicative (you currently need about 3 days to beat humans).
- bcheung 8y agoTransfer learning is about the best we can do right now. Using a fully trained ResNet / XCeptionNet and then tacking on your own layers after the end is within reach to hobbyists with just a single GPU on their desktop. There's still a decent amount of learning for the user even with pre-trained models.
- mark_l_watson 8y ago+1 this is what I do for my at home (non work) experiments in using word embedding and RNNs for generative text summarization. Using transfer learning makes this affordable as a hobby project.